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Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions

Wellmann, Marie-Constanze

Abstract (englisch):
This study aims to identify model parameters describing atmospheric conditions such as wind shear and CCN concentration which lead to large uncertainties in the prediction of deep convective clouds.
In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation and the size distribution of hail.
This dataset contains the processed model output and the generated emulators for three trigger mechanisms of deep convection (warm bubble, cold pool, orography).

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Forschungsbereich Troposphäre (IMK-TRO)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Forschungsdaten
Publikationsjahr 2018
Erstellungsdatum 26.06.2018
Identifikator DOI (KIT): 10.5445/IR/1000085083
KITopen-ID: 1000085083
Lizenz Creative Commons Namensnennung 4.0 International

The csv-files contain the processed model output (spatio-temporal means or maximum values) for output parameters of interest. This dataset was used to train the emulators which are also included as R workspaces. The R package "Sensitivity" is necessary to perform sensitivity analyses using the emulators.

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